DNN Face Detection Confidence — Part 3

Following from the previous post, I tried to load the caffe weights used in this example in the jetson optimized inference example; the model could not be loaded, so I guess the architecture / format was not compatible (they are both caffe models for object detection). On the plus side, I managed to compile and run the DNN face detection code from the opencv examples! The problem was the arguments not being passed properly. (Amazing how many code examples I’m finding that don’t actually work out without modification.)

The good news is that the model and opencv code work very well, actually very very well. In my two hour test with no faces, and a confidence threshold set to 0.1, the max confidence for non faces was only .19! Compare this to the model / jetson inference code, where the same conditions lead to non-faces being recognized with confidence as high as 0.96! The following plot shows the results of the test:

I had to clip the first 1000 or so data samples because my partially visible face was present and that caused spikes in confidence as high as 0.83! The implication is that this detector is much more sensitive to partial / profile faces and that may mean that viewers would have to really look away from the Zombie Formalist for it to generate new images. Technically, I don’t want it to detect profiles as faces. The next stage is to do a test with face present and determine what the range of confidence is and how much of a problem face profile detection causes…